Project
Mark Kirby
23/12/2020
Final Project
Section 1: Analysis
For the analysis section of this project I have chosen to examine a dataset containing information about parks and open spaces in south county Dublin which can be found at the following website: https://data.gov.ie/dataset/parks-and-open-spaces1
This dataset contains information on the size of the space, the category the space falls into, the location of the space and several other variables pertaining to the facilities available at the space.
1.1 Data Cleaning
As usual the first step in any analysis is to read in the data.
# Reading in the data to be analysed
# Keeping character fields as characters
parks <- read.csv('Parks_and_Recreation.csv', stringsAsFactors = FALSE)
# Changing the name of the first column in the data
names(parks)[1] <- "OBJECTID"
head(parks)The data contains 69 rows and 32 columns.
Once the data has been read in we check for missing values, redundant variables or anything else strange that might be present in the data which needs to be removed or fixed before we carry out any analysis. Firstly we check for NA’s in the data.
## OBJECTID groupname grouptypename
## 0 0 0
## typeid Name Address
## 0 0 0
## Telephone Website Parking
## 0 0 0
## DisabilityAccess SummaryActivities SDCC_Owned
## 0 69 0
## SourceFunding Playground AdultExerciseEquipment
## 69 0 0
## RoseGarden FairyWood PetFarm
## 0 0 0
## CycleTrack Allotments CaravanPark
## 0 0 0
## Sports_PlayingPitches SensoryGarden OutdoorGym
## 0 0 0
## CCTV Fishery created_user
## 0 0 0
## created_date last_edited_user last_edited_date
## 0 0 0
## OpeningHours ShapeSTArea
## 0 0We can see from the output that the variables SourceFunding and SummaryActivities contain only missing values, therefore we remove them from the data. Next we check the data for blank values
## OBJECTID groupname grouptypename
## 0 0 0
## typeid Name Address
## 0 0 0
## Telephone Website Parking
## 0 0 0
## DisabilityAccess SDCC_Owned Playground
## 0 0 0
## AdultExerciseEquipment RoseGarden FairyWood
## 0 0 1
## PetFarm CycleTrack Allotments
## 0 0 1
## CaravanPark Sports_PlayingPitches SensoryGarden
## 0 0 0
## OutdoorGym CCTV Fishery
## 0 1 4
## created_user created_date last_edited_user
## 67 67 0
## last_edited_date OpeningHours ShapeSTArea
## 0 53 0# Removing mostly blank columns
parks <- subset(parks, select = -c(created_user, created_date, OpeningHours))We can see from the previous output that the columns created_user, created_date and OpeningHours are filled with mostly blank values therefore I have decided to remove them from the data.
We can also see that the columns Allotments, CCTV, FairyWood and Fishery contain a few blank values. These columns are all indicators of whether or not a certain facility is available at the park. In this case it is appropriate to assume they are not available unless stated otherwise so I have converted these blank values in the value “No”.
# Removing blank values in certain columns
cols_2_edit <- c("Allotments", "CCTV", "FairyWood", "Fishery")
parks[,cols_2_edit][parks[,cols_2_edit] == ''] <- 'No'The next step in cleaning the data is to check how many columns are redundant i.e. only contain one value.
## OBJECTID groupname grouptypename typeid Name Address Telephone Website
## 1 69 68 6 7 69 29 3 2
## Parking DisabilityAccess SDCC_Owned Playground AdultExerciseEquipment
## 1 2 1 1 2 2
## RoseGarden FairyWood PetFarm CycleTrack Allotments CaravanPark
## 1 2 2 2 2 2 2
## Sports_PlayingPitches SensoryGarden OutdoorGym CCTV Fishery last_edited_user
## 1 2 2 2 2 2 1
## last_edited_date ShapeSTArea
## 1 69 69# Remove redundant columns
parks <- subset(parks, select = -c(DisabilityAccess, SDCC_Owned, last_edited_user))We can see that the columns DisabilityAccess, SDCC_Owned, and last_edited_user only contain one value, therefore I have decide to remove them from the data.
After this data cleansing we are left with a smaller dataset with 69 rows and 24 columns.
1.2 Park Area Analysis
Now we will analyse the data and extract some insights about the parks and open spaces in south county Dublin.
Firstly we can look at a table of summary statistics for the size of the parks and open spaces in south Dublin. The unit of measurement for the size of each park is \(m^2\)
sum_tab <- parks %>% summarise(count = n(),
mean = mean(ShapeSTArea),
min = min(ShapeSTArea),
max = max(ShapeSTArea),
median = median(ShapeSTArea),
first_quartile = quantile(ShapeSTArea, prob = 0.25),
third_quartile = quantile(ShapeSTArea, prob = 0.75))
knitr::kable(round(sum_tab))| count | mean | min | max | median | first_quartile | third_quartile |
|---|---|---|---|---|---|---|
| 69 | 151419 | 86 | 1369076 | 69620 | 29079 | 168593 |
From this output we can see that there are 69 parks in South Dublin, the largest park (Corkagh Park) is approximately 1369076\(m^2\) and the smallest park (Glenshane Skate Park) is approximately 86\(m^2\). It is worth noting that this data only contains parks that South Dublin County Council are responsible for so the Phoenix Park is not present in this data. Some other large parks such as Tymon park are also split into several pieces in this data.
The mean value for a park in South Dublin is 151418.9 while the median value is 69619.99.
Next we create a visual depiction of the distribution of park areas in South Dublin.
# Creating a density plot of park area
p <- ggplot(parks, aes(x=ShapeSTArea)) +
geom_density(fill="lightblue") +
scale_x_continuous(labels = scales::comma) +
scale_y_continuous(labels = scales::comma) +
labs(title="Park Area Density Plot",x=expression("Park Area "(m^2)), y = "Density") +
theme(plot.title = element_text(hjust = 0.5))
# Creating a density plot of the log park area for readability
q <- ggplot(parks, aes(x=log(ShapeSTArea))) +
geom_density(fill="lightblue") +
scale_x_continuous(labels = scales::comma) +
scale_y_continuous(labels = scales::comma) +
labs(title="Log of Park Area Density Plot",x=expression("Log Park Area "(m^2)), y = "Density") +
theme(plot.title = element_text(hjust = 0.5))
grid.arrange(p, q, ncol=2)
Figure 1: Density plots for the area of parks in South Dublin.
In Figure 1 we create two density plots. One is a density plot based on the area of parks in South Dublin. The second is a density plot of the log of the area of parks in South Dublin. The second plot is added as the first plot is a little bit difficult to read.
These plots tell us that the majority of parks have areas of less than 250,000\(m^2\) with only a few parks having areas greater than that.
If we examine each park individually we can also see some interesting insights
# Creating a lollipop plot of each parks size
plot1 <- parks %>%
ggplot(aes(x=Name, y=ShapeSTArea)) +
geom_segment( aes(x=Name, xend=Name, y=0, yend=ShapeSTArea), color="skyblue") +
geom_point( color="blue", size=4, alpha=0.6) +
theme_light() +
scale_y_continuous(labels = scales::comma) +
coord_flip() +
labs(title="Park Area Lollipop Plot",
y=expression("Park Area "(m^2)),
x = "Name") +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.y = element_text(color = "grey20", size = 6,
angle = 0, hjust = 1,
vjust = 0, face = "plain") )
# Creating a bar plot of each parks size
plot2 <- parks %>%
mutate(Name = fct_reorder(Name, desc(-ShapeSTArea))) %>%
ggplot( aes(x=Name, y=ShapeSTArea)) +
geom_bar(stat="identity", fill="lightblue", width=.8) +
scale_y_continuous(labels = scales::comma) +
coord_flip() +
labs(title="Park Area Bar Plot",
y=expression("Park Area "(m^2)),
x = "Name") +
theme(axis.text.y = element_text(color = "grey20",
size = 6, angle = 0,
hjust = 1, vjust = 0,
face = "plain"),
plot.title = element_text(hjust = 0.5))
grid.arrange(plot1, plot2, ncol=2)
Figure 2: Plots of individual park area for parks in South Dublin.
Figure 2 shows where each park falls in terms of overall area. As mentioned above we can see that some large parks such as Tymon Park and Griffeen Park have actually been split into different pieces. In the next step we rejoin these pieces and examine the effect it has on the output.
# Creating a new column to store the combined park names
parks$merged_name <- parks$Name
# merging specific park names
parks$merged_name[parks$Name %in% c("Tymon Open Space",
"Tymon Park East",
"Tymon Park West")] <- "Tymon Park"
parks$merged_name[parks$Name %in% c("Dodder Valley Park Cherryfield",
"Dodder Valley Park Firhouse ",
"Dodder Valley Park Kilvere",
"Dodder Valley Park Oldbawn")] <- "Dodder Valley Park"
parks$merged_name[parks$Name %in% c("Greentrees Park Eight Acres",
"Greentrees Park Five Acres " )] <- "Greentrees Park"
parks$merged_name[parks$Name %in% c("Griffeen Valley Park",
"Griffeen Valley Park Extension",
"Griffeen Valley Park Running Park",
"Griffeen Valley Skate Park")] <- "Griffeen Valley Park"
parks$merged_name[parks$Name %in% c("Clondalkin Park",
"Clondalkin Skate Park" )] <- "Clondalkin Park"
# Creating a new df with only combined park names
new_parks <- parks %>%
group_by(merged_name) %>%
summarise(park_area = sum(ShapeSTArea), .groups = 'keep')In the code section above we have combined the parks which have been split into different pieces, namely Clondalkin Park, Dodder Valley Park, Greentrees Park, Griffeen Valley Park and Tymon Park. Below I have recreated the plot in Figure 2 but this time the split parks have been combined.
# Creating a lollipop plot of each parks size
plot1 <- new_parks %>%
ggplot(aes(x=merged_name, y=park_area)) +
geom_segment( aes(x=merged_name, xend=merged_name, y=0, yend=park_area), color="skyblue") +
geom_point( color="blue", size=4, alpha=0.6) +
theme_light() +
scale_y_continuous(labels = scales::comma) +
coord_flip() +
labs(title="Park Area Lollipop Plot",
y=expression("Park Area "(m^2)),
x = "Merged Name") +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.y = element_text(color = "grey20", size = 6,
angle = 0, hjust = 1,
vjust = 0, face = "plain") )
# Creating a bar plot of each parks size
plot2 <- new_parks %>%
ggplot( aes(x=reorder(merged_name, park_area, sum), y=park_area)) +
geom_bar(stat="identity", fill="lightblue", width=.8) +
scale_y_continuous(labels = scales::comma) +
coord_flip() +
labs(title="Park Area Bar Plot",
y=expression("Park Area "(m^2)),
x = "Merged Name") +
theme(axis.text.y = element_text(color = "grey20",
size = 6, angle = 0,
hjust = 1, vjust = 0,
face = "plain"),
plot.title = element_text(hjust = 0.5))
grid.arrange(plot1, plot2, ncol=2)
Figure 3: Plots of individual park area for parks in South Dublin with split parks combined.
We can see from Figure 3 that combining the split parks has changed the results in terms of which parks has the biggest area. Tymon Park is now the largest park and Dodder Valley Park and Griffeen Park have moved higher up the ranking.
The next piece of analysis in this section is based on the differences between each type of park in the data and uses the original park data.
1.2 Park Type Analysis
This data contains 6 park types, namely Open Space Incorporating Pitches, Neighbourhood Park, Open Space, Regional Park, Skate Park, Village park and Village Park. We can see a summary for each park type in the table below.
sum_tab2 <- parks %>%
group_by(grouptypename) %>%
summarise(count = n(),
mean = mean(ShapeSTArea),
min = min(ShapeSTArea),
max = max(ShapeSTArea),
median = median(ShapeSTArea),
first_quartile = quantile(ShapeSTArea, prob = 0.25),
third_quartile = quantile(ShapeSTArea, prob = 0.75),
.groups = 'keep')
# Rounding summary values
sum_tab2[,-1] <- round(sum_tab2[,-1])
# Displaying the output in a table
knitr::kable(sum_tab2, col.names = c('Park Type', 'count', 'mean',
'min', 'max', 'median',
'first_quartile',
'third_quartile'))| Park Type | count | mean | min | max | median | first_quartile | third_quartile |
|---|---|---|---|---|---|---|---|
| Neighbourhood Park | 27 | 137064 | 10950 | 479932 | 113599 | 58954 | 180183 |
| Open Space | 4 | 30039 | 3444 | 75369 | 20672 | 10103 | 40608 |
| Open Space Incorporating Pitches | 20 | 59301 | 12374 | 177385 | 36353 | 19674 | 73793 |
| Regional Park | 13 | 418085 | 31587 | 1369076 | 424939 | 102431 | 467451 |
| Skate Park | 3 | 478 | 86 | 789 | 560 | 323 | 675 |
| Village park | 2 | 2237 | 1848 | 2627 | 2237 | 2043 | 2432 |
The table above shows that regional parks have the largest mean area (418085\(m^2\)) while skate parks have the lowest mean area (478\(m^2\)). A box plot is an interesting visual representation of this data and can be seen below.
# Box plot for area of park
p1 <- ggplot(parks,
aes(x=reorder(grouptypename,
ShapeSTArea,
FUN = median),
y=ShapeSTArea,
fill=grouptypename)) +
geom_boxplot(outlier.shape=4, outlier.size=4) +
coord_flip() +
scale_y_continuous(labels = scales::comma) +
labs(title="Park Area Box Plot",
y=expression("Park Area "(m^2)),
x = "Park Type",
fill = "Park Type") +
theme(plot.title = element_text(hjust = 0.5))
# Box plot for log area of park for readability
p2 <- ggplot(parks,
aes(x=reorder(grouptypename,
log(ShapeSTArea),
FUN = median),
y=log(ShapeSTArea),
fill=grouptypename)) +
geom_boxplot(outlier.shape=4, outlier.size=4) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=0.4, binwidth = 0.4) +
coord_flip() +
labs(title="Log of Park Area Box Plot",
y=expression("Log Park Area "(m^2)),
x = "Park Type",
fill = "Park Type") +
theme(plot.title = element_text(hjust = 0.5))
# Displaying plots
grid.arrange(p1, p2, ncol=1)
Figure 4: Box plots for the area of parks in South Dublin.
The first plot in Figure 4 is a boxplot of the park area for each park type, we can see that regional parks have the highest median park area as we would expect from the previous table. However it is quite difficult to see the differences between the other park types in this plot. Therefore I have also created a boxplot of the log of the park area in South Dublin.
In the second plot it is easier to see the differences between each park type because we have used the log of the park area instead of the park area. The dots on the second plot are a representation of where the log of each park area lies in the plot. The logs have been put into bins in this plot to aid with readability.
In the second plot we can see a clear hierarchy in term of the types of park and the corresponding park areas.
1.3 Facility Analysis
The next step in my analysis is to examine what type of facilities are available at each type of park.
The data contains indicator variables which contain data on whether certain facilities are available at certain parks. Some examples of such facilities are caravan parks, parking and rose gardens. Firstly we look at which facilities are the most widely available. To do this we need to do some data preparation.
# creating a data tibble with the count of availability for each facility type
facility_counts <- parks %>%
group_by(grouptypename) %>%
summarise(Parking = sum(Parking == 'Yes'),
Playgrounds = sum(Playground == 'Yes'),
Exercise = sum(AdultExerciseEquipment == 'Yes'),
Rose_Garden = sum(RoseGarden == 'Yes'),
Fairy_Wood = sum(FairyWood == 'Yes'),
Pet_Farm = sum(PetFarm == 'Yes'),
Cycle_Track = sum(CycleTrack == 'Yes'),
Allotments = sum(Allotments == 'Yes'),
Caravan_Park = sum(CaravanPark == 'Yes'),
Pitches = sum(Sports_PlayingPitches == 'Yes'),
Sensory_Garden = sum(SensoryGarden == 'Yes'),
Gym = sum(OutdoorGym == 'Yes'),
CCTV = sum(CCTV == 'Yes'),
Fishery = sum(Fishery == 'Yes'),
.groups = 'keep')
# Using the melt function to reshape the data
facility_dat <- melt(facility_counts, id.vars=c("grouptypename"))
# Counting the occurrence of the availability of each facility
overall_facilities <- facility_dat %>%
group_by(variable) %>%
summarise(count = sum(value), .groups = 'keep')
# Printing output
head(overall_facilities)Once we have prepared the data we can use a bar plot to analyse the availability of each facility.
# converting tibble to df
as.data.frame(overall_facilities) %>%
# setting the order for bars
mutate(variable = fct_reorder(variable, desc(-count))) %>%
# Creating the bar plot
ggplot( aes(x=variable, y=count)) +
geom_bar(stat="identity", fill="lightblue", width=.8) +
coord_flip() +
labs(title="Facility Availability",y="Count of Facility Availability", x = "Facility") +
theme(axis.text.y = element_text(color = "grey20", size = 10, angle = 0, hjust = 1, vjust = 0, face = "plain"),
plot.title = element_text(hjust = 0.5))
Figure 5: Bar plot of facility availability.
Figure 5 shows that playing pitches are the most common facility in the parks in this dataset. Sensory gardens and fisheries are the least common facility in these parks. Notably there has been a push in recent times by South Dublin County Council to put more outdoor gyms and outdoor exercise equipment in parks and open spaces. This is reflected in the data as these two facilities are the 4th and 5th most available facilities in these parks.
Next we examine the spread of facilities across the different park types in the data.
# Creating a vector of colours
colvec <- distinctColorPalette(length(unique(facility_dat$variable)))
# Creating a barplot of the facility availability by park type
facility_dat[facility_dat$value>0,] %>%
ggplot(aes(x = variable, y = value, fill = variable)) +
geom_col(position = "dodge") +
facet_grid(~grouptypename,
scales = "free_x",
space = "free_x",
switch = "x") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
strip.background = element_blank(),
strip.text.x = element_text(angle = 90),
plot.title = element_text(hjust = 0.5)) +
labs(title="Facility Availability by Park Type",
y= 'Count of Facility Availability',
x = "Park Type",
fill = "Facility") +
scale_fill_manual(values = colvec)
Figure 6: Bar plot of facility availability broken out by park type.
Figure 6 shows the breakdown of facility availability for each park type. We can see that both skate parks and village parks have only one facility available which is parking. Regional parks have the broadest range of facilities available as we would expect because overall they are usually larger than other park types. Neighborhood parks also have a broad range of facilities available including playing pitches which are not available to the same extent in regional parks.
Next we will create a map of where each of these parks is located in South Dublin.
1.4 Mapping
This data came from the Irish government data website (https://data.gov.ie/dataset/parks-and-open-spaces1) and for this particular dataset a shapefile is also included. This shapefile can be used to create a choropleth map which can be overlayed onto a map of South Dublin.
The first step in this process is to load the shapefile
# Temporarily turning off warnings as readOGR gives a warning that has no effect on the data
# Saving current warning settings
ow <- options("warn")$warn
# Turning off warnings
options("warn"=-1)
# Reading the shapefile
my_spdf <- readOGR(
dsn= "Parks_and_Recreation-shp" ,
layer="Parks_and_Recreation",
verbose=FALSE)
# Restoring warning settings
options("warn"=ow)Once the shapefile has been read in we need to get the background for our map. This is done using the get_stamenmap() function which takes longitude and latitude coordinates and returns a map of the area specified in the coordinates. This function uses the open source map utility called Stamen maps.
# Extracting the required map using get_stamenmap
map <- get_stamenmap(bbox = c(left = -6.5,
bottom = 53.25,
right = -6.25,
top = 53.38),
maptype = 'terrain',
zoom = 14)
# using ggmap to plot
ggmap(map) +
labs(title="Map of South Dublin",x="Longitude", y = "Latitude") +
theme(plot.title = element_text(hjust = 0.5))
Figure 7: Map of South Dublin.
Next we need to add the parks to this background. Unfortunately the shapefile is not using the usual longitude and latitude coordinate system so some pre-processing is required.
# Changing coordinate system
shp <- spTransform(my_spdf, CRS("+proj=longlat +datum=WGS84"))
# tidying the shapefile
shp_clean <- broom::tidy(shp)## Regions defined for each Polygons# Adding park type to cleaned data
shp_clean$Park_Type <- my_spdf$grouptypen[as.numeric(shp_clean$id) + 1]
# Adding park name to cleaned data
shp_clean$Name <- my_spdf$Name[as.numeric(shp_clean$id) + 1]
# Adding park size to cleaned data
shp_clean$Size <- paste(round(my_spdf$ShapeSTAre[as.numeric(shp_clean$id) + 1]), expression(m^2))
# Creating the plot
ggmap(map) +
geom_polygon(data = shp_clean,
aes(x = long, y = lat, group = group, fill = Park_Type),
colour = "black") +
labs(title="Map of South Dublin",x="Longitude",
y = "Latitude", fill = "Park Type") +
theme(plot.title = element_text(hjust = 0.5))
Figure 8: Map of South Dublin.
This map shows the locations of the parks in this dataset on a real map of South Dublin however, Some of the smaller parks are quite difficult to see. To combat this issue we also show a map with highlights all parks using a point.
data_df <- as.data.frame(coordinates(shp))
names(data_df) <- c("lon", "lat")
data_df$Park_Type <- my_spdf$grouptypen
data_df$Size <- my_spdf$ShapeSTAre
data_df$Name <- my_spdf$Name
colvec <- distinctColorPalette(length(unique(data_df$Park_Type)))
ggmap(map) +
geom_point(aes(lon, lat, colour = Park_Type), size = 2.5, data = data_df) +
scale_fill_manual(values = colvec) +
labs(title="Map of South Dublin",x="Longitude",
y = "Latitude", colour = "Park Type") +
theme(plot.title = element_text(hjust = 0.5))
Figure 9: Map of South Dublin.
From Figure 9 all parks in the data set can be seen on the map with the colour indicating the type of park.
I have also created animated versions of the map plots, unfortunately they will not be usable in pdf format therefore I have stored the animated versions on my github account here: https://markkirby95.github.io/STAT40730-Data-Prog-with-R_Part_1/
I am including the code for this graphs below.
# Saving current warning settings
ow <- options("warn")$warn
# Turning off warnings
options("warn"=-1)
# Creating the plot
ani_plot <- ggmap(map) +
geom_polygon(data = shp_clean,
aes(x = long, y = lat, group = group, fill = Park_Type, Name = Name, Size = Size),
colour = "black") +
labs(title="Map of South Dublin",x="Longitude",
y = "Latitude", fill = "Park Type") +
theme(plot.title = element_text(hjust = 0.5))
# Not allowing scientific notation
options(scipen=999)
# Restoring warning settings
options("warn"=ow)
# Creating the animated plot
ggplotly(ani_plot, tooltip = c("Park_Type", "Name", "Size"))## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.